In another demonstration of the great value of the amassed data in DataShop, Koedinger, Yudelson, & Pavlik (in press) used eight of the stored datasets stored to address a long-standing debate between competing theories of transfer of learning: faculty theory versus an identical elements or component theory (cf., Singley & Anderson, 1989). They developed statistical models of these alternative theories and found that the component theory provides better prediction accuracy and better explanatory power than the faculty theory. These results provide further support for KLI Framework construct of knowledge components.

Domain-general models of learning. One fundamental contribution of the CMDM team was an AI journal paper on the integration of representation learning into skill learning (Li et al., 2015). The paper presented a computational model that extended our machine learning simulation of student learning. SimStudent, providing a precise theoretical account of inductive learning processes in the KLI framework. This effort continued a LearnLab theme of demonstrating the relevance of language learning processes (i.e., grammar learning as a form of representation learning) in science and mathematics learning (cf., Koedinger & McLaughlin, 2010; Koedinger & McLaughlin 2016).